Deep convolutional networks for pancreas segmentation in CT imaging

04/15/2015
by   Holger R. Roth, et al.
0

Automatic organ segmentation is an important prerequisite for many computer-aided diagnosis systems. The high anatomical variability of organs in the abdomen, such as the pancreas, prevents many segmentation methods from achieving high accuracies when compared to other segmentation of organs like the liver, heart or kidneys. Recently, the availability of large annotated training sets and the accessibility of affordable parallel computing resources via GPUs have made it feasible for "deep learning" methods such as convolutional networks (ConvNets) to succeed in image classification tasks. These methods have the advantage that used classification features are trained directly from the imaging data. We present a fully-automated bottom-up method for pancreas segmentation in computed tomography (CT) images of the abdomen. The method is based on hierarchical coarse-to-fine classification of local image regions (superpixels). Superpixels are extracted from the abdominal region using Simple Linear Iterative Clustering (SLIC). An initial probability response map is generated, using patch-level confidences and a two-level cascade of random forest classifiers, from which superpixel regions with probabilities larger 0.5 are retained. These retained superpixels serve as a highly sensitive initial input of the pancreas and its surroundings to a ConvNet that samples a bounding box around each superpixel at different scales (and random non-rigid deformations at training time) in order to assign a more distinct probability of each superpixel region being pancreas or not. We evaluate our method on CT images of 82 patients (60 for training, 2 for validation, and 20 for testing). Using ConvNets we achieve average Dice scores of 68 pancreas segmentation, using a deep learning approach and compares favorably to state-of-the-art methods.

READ FULL TEXT

page 2

page 3

page 4

page 6

research
06/22/2015

DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation

Automatic organ segmentation is an important yet challenging problem for...
research
07/31/2014

A Bottom-Up Approach for Automatic Pancreas Segmentation in Abdominal CT Scans

Organ segmentation is a prerequisite for a computer-aided diagnosis (CAD...
research
04/15/2015

Anatomy-specific classification of medical images using deep convolutional nets

Automated classification of human anatomy is an important prerequisite f...
research
01/31/2017

Spatial Aggregation of Holistically-Nested Convolutional Neural Networks for Automated Pancreas Localization and Segmentation

Accurate and automatic organ segmentation from 3D radiological scans is ...
research
06/24/2016

Spatial Aggregation of Holistically-Nested Networks for Automated Pancreas Segmentation

Accurate automatic organ segmentation is an important yet challenging pr...
research
01/29/2016

Deep convolutional networks for automated detection of posterior-element fractures on spine CT

Injuries of the spine, and its posterior elements in particular, are a c...
research
08/01/2017

CNN Cascades for Segmenting Whole Slide Images of the Kidney

Due to the increasing availability of whole slide scanners facilitating ...

Please sign up or login with your details

Forgot password? Click here to reset